ArticleGenome biology2025
ScIsoX: a multidimensional framework for measuring isoform-level transcriptomic complexity in single cells.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- ICON: An isoform-aware hierarchical random forest model for cell type classification.bioRxiv : the preprint server for biology · 2026Article
- Hypatia: Comparative Isoform Profiling Across Cell Populations from Long-Read Single-Cell Transcriptomes.bioRxiv : the preprint server for biology · 2026Article
- Bioinformatics frameworks for single-cell long-read sequencing: unlocking isoform-level resolution.Briefings in bioinformatics · 2025Review
- ScIsoX: a multidimensional framework for measuring isoform-level transcriptomic complexity in single cells.Genome biology · 2025Article
- Advancing automated cell type annotation with large language models and single-cell isoform sequencing.Computational and structural biotechnology journal · 2025Review
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Authors and funding
2 authors.
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Abstract
Single-cell isoform analysis enables high-resolution characterization of transcript expression, yet analytical frameworks to systematically measure transcriptomic complexity are lacking. Here, we introduce ScIsoX, a computational framework that integrates a novel hierarchical data structure, a suite of complexity metrics, and dedicated visualization tools for isoform-level analysis. ScIsoX supports systematic exploration of global and cell-type-specific isoform expression patterns arising from alternative splicing, revealing multidimensional complexity signatures across diverse datasets-insights often missed by conventional gene-level approaches. We demonstrate the utility of ScIsoX across multiple real-world single-cell isoform sequencing datasets, showcasing its potential as a general framework for transcriptomic complexity analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.